Associating Uncertainty to Extended Poses for on Lie Group IMU Preintegration With Rotating Earth

The recently introduced matrix group SE2(3) provides a 5×5 matrix representation for the orientation, velocity and position of an object in the 3-D space, a triplet we call "extended pose". In this paper we build on this group to develop a theory to associate uncertainty with extended poses represented by 5×5 matrices. Our approach is particularly suited to describe how uncertainty propagates when the extended pose represents the state of an Inertial Measurement Unit (IMU). In particular it allows revisiting the theory of IMU preintegration on manifold and reaching a further theoretic level in this field. Exact preintegration formulas that account for rotating Earth, that is, centrifugal force and Coriolis force, are derived as a byproduct, and the factors are shown to be more accurate. The approach is validated through extensive simulations and applied to sensor-fusion where a loosely-coupled fixed-lag smoother fuses IMU and LiDAR on one hour long experiments using our experimental car. It shows how handling rotating Earth may be beneficial for long-term navigation within incremental smoothing algorithms.

[1]  Bernhard P. Wrobel,et al.  Multiple View Geometry in Computer Vision , 2001 .

[2]  Tim D. Barfoot,et al.  Full STEAM ahead: Exactly sparse gaussian process regression for batch continuous-time trajectory estimation on SE(3) , 2015, 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

[3]  Axel Barrau,et al.  A Mathematical Framework for IMU Error Propagation with Applications to Preintegration , 2020, 2020 IEEE International Conference on Robotics and Automation (ICRA).

[4]  Frank Dellaert,et al.  iSAM2: Incremental smoothing and mapping using the Bayes tree , 2012, Int. J. Robotics Res..

[5]  Peter Cheeseman,et al.  On the Representation and Estimation of Spatial Uncertainty , 1986 .

[6]  Juan Andrade-Cetto,et al.  Joint on-manifold self-calibration of odometry model and sensor extrinsics using pre-integration , 2019, 2019 European Conference on Mobile Robots (ECMR).

[7]  Frank Dellaert,et al.  Factor graph based incremental smoothing in inertial navigation systems , 2012, 2012 15th International Conference on Information Fusion.

[8]  Guoquan Huang,et al.  Closed-form preintegration methods for graph-based visual–inertial navigation , 2018, Int. J. Robotics Res..

[9]  Salah Sukkarieh,et al.  Visual-Inertial-Aided Navigation for High-Dynamic Motion in Built Environments Without Initial Conditions , 2012, IEEE Transactions on Robotics.

[10]  Gregory S. Chirikjian,et al.  Nonparametric Second-order Theory of Error Propagation on Motion Groups , 2008, Int. J. Robotics Res..

[11]  Timothy D. Barfoot,et al.  A White-Noise-on-Jerk Motion Prior for Continuous-Time Trajectory Estimation on SE(3) , 2018, IEEE Robotics and Automation Letters.

[12]  Timothy D. Barfoot,et al.  A Data-Driven Motion Prior for Continuous-Time Trajectory Estimation on SE(3) , 2020, IEEE Robotics and Automation Letters.

[13]  Michael Bosse,et al.  Keyframe-based visual–inertial odometry using nonlinear optimization , 2015, Int. J. Robotics Res..

[14]  Shi-Sheng Huang,et al.  Tightly-Coupled Monocular Visual-Odometric SLAM Using Wheels and a MEMS Gyroscope , 2018, IEEE Access.

[15]  Henawy John,et al.  Accurate IMU Factor Using Switched Linear Systems for VIO , 2020, IEEE Transactions on Industrial Electronics.

[16]  F. Dellaert Factor Graphs and GTSAM: A Hands-on Introduction , 2012 .

[17]  Gregory S. Chirikjian,et al.  Error propagation on the Euclidean group with applications to manipulator kinematics , 2006, IEEE Transactions on Robotics.

[18]  Dehann Fourie,et al.  Multi-modal and inertial sensor solutions for navigation-type factor graphs , 2017 .

[19]  Frank Dellaert,et al.  On-Manifold Preintegration for Real-Time Visual--Inertial Odometry , 2015, IEEE Transactions on Robotics.

[20]  Maurice Fallon,et al.  Preintegrated Velocity Bias Estimation to Overcome Contact Nonlinearities in Legged Robot Odometry , 2019, 2020 IEEE International Conference on Robotics and Automation (ICRA).

[21]  Audrey Giremus,et al.  Continuous-Discrete Extended Kalman Filter on Matrix Lie Groups Using Concentrated Gaussian Distributions , 2014, Journal of Mathematical Imaging and Vision.

[22]  Andreas Geiger,et al.  Vision meets robotics: The KITTI dataset , 2013, Int. J. Robotics Res..

[23]  Manuela Herman,et al.  Aided Navigation Gps With High Rate Sensors , 2016 .

[24]  Frank Dellaert,et al.  Information fusion in navigation systems via factor graph based incremental smoothing , 2013, Robotics Auton. Syst..

[25]  Vijay Kumar,et al.  Tightly-coupled monocular visual-inertial fusion for autonomous flight of rotorcraft MAVs , 2015, 2015 IEEE International Conference on Robotics and Automation (ICRA).

[26]  Maani Ghaffari Jadidi,et al.  Hybrid Contact Preintegration for Visual-Inertial-Contact State Estimation Using Factor Graphs , 2018, 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

[27]  Kevin C. Wolfe,et al.  Bayesian Fusion on Lie Groups , 2011 .

[28]  Gregory S. Chirikjian,et al.  The Banana Distribution is Gaussian: A Localization Study with Exponential Coordinates , 2012, Robotics: Science and Systems.

[29]  Dinesh Atchuthan,et al.  A micro Lie theory for state estimation in robotics , 2018, ArXiv.

[30]  Paul Timothy Furgale,et al.  Associating Uncertainty With Three-Dimensional Poses for Use in Estimation Problems , 2014, IEEE Transactions on Robotics.

[31]  Sylvain Calinon Gaussians on Riemannian Manifolds: Applications for Robot Learning and Adaptive Control , 2020, IEEE Robotics & Automation Magazine.

[32]  Ming Liu,et al.  Tightly Coupled 3D Lidar Inertial Odometry and Mapping , 2019, 2019 International Conference on Robotics and Automation (ICRA).

[33]  Jean-Philippe Condomines,et al.  Unscented Kalman filtering on Lie groups , 2017, 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

[34]  José A. Castellanos,et al.  On the Importance of Uncertainty Representation in Active SLAM , 2018, IEEE Transactions on Robotics.

[35]  Eren Allak,et al.  Covariance Pre-Integration for Delayed Measurements in Multi-Sensor Fusion , 2019, 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

[36]  Shoudong Huang,et al.  Gaussian Process Preintegration for Inertial-Aided State Estimation , 2020, IEEE Robotics and Automation Letters.

[37]  Axel Barrau,et al.  Invariant Kalman Filtering , 2018, Annu. Rev. Control. Robotics Auton. Syst..

[38]  Davide Scaramuzza,et al.  A Tutorial on Quantitative Trajectory Evaluation for Visual(-Inertial) Odometry , 2018, 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

[39]  Liu Ren,et al.  Analytic Combined IMU Integration (ACI2) For Visual Inertial Navigation , 2020, 2020 IEEE International Conference on Robotics and Automation (ICRA).

[40]  Axel Barrau,et al.  Linear observed systems on groups , 2019, Syst. Control. Lett..

[41]  Frank Dellaert,et al.  Eliminating conditionally independent sets in factor graphs: A unifying perspective based on smart factors , 2014, 2014 IEEE International Conference on Robotics and Automation (ICRA).

[42]  Timothy D. Barfoot,et al.  State Estimation for Robotics , 2017 .

[43]  Anish K Mampetta A Lie group formulation of Kinematics and Dynamics of serial Manipulators Course Project Report 16-741 : Mechanics of Manipulation , 2006 .

[44]  Ryan M. Eustice,et al.  Characterizing the Uncertainty of Jointly Distributed Poses in the Lie Algebra , 2019, IEEE Transactions on Robotics.

[45]  J.L. Crassidis,et al.  Sigma-point Kalman filtering for integrated GPS and inertial navigation , 2005, IEEE Transactions on Aerospace and Electronic Systems.

[46]  Yun-Hui Liu,et al.  Visual-Odometric Localization and Mapping for Ground Vehicles Using SE(2)-XYZ Constraints , 2019, 2019 International Conference on Robotics and Automation (ICRA).

[47]  Wolfram Burgard,et al.  A real-time algorithm for mobile robot mapping with applications to multi-robot and 3D mapping , 2000, Proceedings 2000 ICRA. Millennium Conference. IEEE International Conference on Robotics and Automation. Symposia Proceedings (Cat. No.00CH37065).

[48]  Paul Chauchat,et al.  Smoothing algorithms for navigation, localisation and mapping based on high-grade inertial sensors. (Algorithmes de lissage pour la navigation, la localisation et la cartographie, basés sur des capteurs inertiels haute qualité) , 2020 .

[49]  Frank Dellaert,et al.  Factor Graphs for Robot Perception , 2017, Found. Trends Robotics.

[50]  Tat-Jun Chin,et al.  Outlier-Robust Manifold Pre-Integration for INS/GPS Fusion , 2019, 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

[51]  Axel Barrau,et al.  The Invariant Extended Kalman Filter as a Stable Observer , 2014, IEEE Transactions on Automatic Control.

[52]  G. Chirikjian Stochastic models, information theory, and lie groups , 2012 .

[53]  Kevin Eckenhoff,et al.  Direct visual-inertial navigation with analytical preintegration , 2017, 2017 IEEE International Conference on Robotics and Automation (ICRA).

[54]  Shaojie Shen,et al.  VINS-Mono: A Robust and Versatile Monocular Visual-Inertial State Estimator , 2017, IEEE Transactions on Robotics.

[55]  Frank Dellaert,et al.  IMU Preintegration on Manifold for Efficient Visual-Inertial Maximum-a-Posteriori Estimation , 2015, Robotics: Science and Systems.